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Record W4411459711 · doi:10.1021/acsomega.5c02640

Low-Power Ternary Bipolar Memristor of Naturally Oxidized Porous Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub> MXene Flakes

2025· article· en· W4411459711 on OpenAlexaff
Seyed Mehdi Sattari‐Esfahlan, Ali Shayesteh Zeraati, Jae‐Hyun Lee, Uttandaraman Sundararaj, Reza Rahighi

Bibliographic record

VenueACS Omega · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMXene and MAX Phase Materials
Canadian institutionsUniversity of TorontoUniversity of Calgary
FundersTechnische Universität Wien Bibliothek
KeywordsTernary operationMemristorMaterials sciencePorosityElectrical engineeringComposite materialComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract The multilevel characteristic of a single memristor cell offers a promising alternative to using multiple FETs for the same data storage capacity. Ti3C2Tx MXene, with its atomically thin structure and tunable surface-dependent electronic properties, is a strong candidate for multifunctional electronic materials and devices. However, the high conductivity of as-prepared Ti3C2Tx flakes limits their use in active electronic devices. Here, we present naturally oxidized porous Ti3C2Tx MXene flakes as promising materials for ion-based resistive switching memory (RSM) applications. The fabricated devices exhibit reproducible ternary memory behavior with low operating voltage, stable retention, and robust performance. We suggest that the drift of oxygen ions and the formation of metallic filaments in oxidized porous Ti3C2Tx are responsible for observed resistive switching. Our findings demonstrate that oxidized porous Ti3C2Tx MXene can be an alternative to traditional resistive switching materials and enhance multilevel memory technology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.225
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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